Learn Cycle, Natural and Ruby

In this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

Cycle.js is a framework where your app is described as a simple function taking an event stream as input and outputting an event stream. 

Cycle.js builds on functional and reactive streams and is a JavaScript framework. What does that mean? In the course, André Staltz, the creator of Cycle.js, will explain it to us.

We'll start this course with an empty project and build out our own toy version of Cycle.js to understand exactly what's going on under the hood. When we have a solid base of how Cycle.js works we will move on to use the Cycle.js API and build an app using the Model-View-Intent pattern.

Cycle.js is a framework where your app is described as a simple function taking an event stream as input and outputting an event stream.

cycle

Cycle.js Fundamentals

We are starting to get a better architecture for these UI apps. But we still have a problem of repeating code for the sliders, since they share so much in common in looks and functionality. This lessons shows how we can create a generic labeled slider as a main() function receiving properties as sources.

Create a Generic Cycle.js Component

Our app is not yet a BMI calculator, because it only has two sliders, but doesn't show the calculated BMI number. In this lesson we will learn how to export any stream from a child component and use it in the parent component, in order to display the BMI calculation on the DOM.

Export Values from Components through Sinks in Cycle.js

This lesson shows what can be learned next as a continuation of this course, and gives a recap on the core concepts: main for pure logic, drivers for side effects, run() to connect main and drivers, sources for read effects, sinks for write effects, and nesting Cycle.js apps to work as components.

Overview of Cycle.js

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

rails

Expanding on [Part 1](https://egghead.io/lessons/angularjs-rails-todo-api-part-1), this lesson will look at how to bootstrap AngularJS in a rails application as part of the asset pipeline. With Angular available, you'll see how to communicate with the Rails API using $resource.

Rails Todo API Part 2

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

Identify the most important words in a document using tf-idf in Natural

Let's see next how to use the HTTP driver to generate requests and capture responses. We will build a small app to fetch a single piece of data from a REST server and display that on the DOM.

Fetch Data using the Cycle.js HTTP Driver

Once we have a labeled slider component inside the main function, we should be able to create another labeled slider. This lesson teaches how to accomplish that, and make sure those two instances work independently of each other.

Use Multiple Independent Instances of a Cycle.js Component

We added classNames to pre-processing and post-processing steps when calling the LabeledSlider, in order to instances independent of each other. It would be better if we could hide these steps away. This lesson introduces the isolate() helper function to achieve that.

Isolate Component Instances in Cycle.js

This lesson is the first in an introduction to Cycle.js. We will see how to build from scratch a toy version of Cycle.js. See how to use RxJS to create simple web apps, and how to separate logic from effects.

Separate Logic from Effects in Cycle.js

We need to give structure to our application with logic and effects. This lessons shows how we can organize our code into two parts: main() function for logic, and effects functions for effects.

Organize Code in the Main and Effect Functions

How can we show one string on the DOM, and a completely different string on Console log? This lesson shows how we can make our main function return multiple Observables, each one targeted at a different type of effect.

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